Methods of health degradation estimation and fault isolation for system health monitoring

ABSTRACT

Methods and systems for fault identification and mitigation in an engine system. A state observer obtains current state information from the engine system, and a feature calculator uses data obtained from the state observer to calculate one or more feature indicators, which are monitored by a health estimator for the occurrence of a change using one or more change probability models. When the health estimator identifies a change, a fault isolator determines a component of the engine system that is subject to fault or health deterioration.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. Pat. ApplicationSerial Number 17/407,047, filed Aug. 19, 2021, titled METHODS OF HEALTHDEGRADATION ESTIMATION AND FAULT ISOLATION FOR SYSTEM HEALTH MONITORING,the disclosure of which is incorporated herein by reference.

BACKGROUND

Vehicle engine systems have grown incredibly complex as new technologiesare introduced and new standards for emissions and fuel efficiency areadded. Model based control methods have been introduced to facilitatethe management of complex technology and requirements. Hardwarecomponents can deviate from nominal behavior through aging, wear, andother degradation or failure scenarios, rendering a model used in thecontrol method out of alignment with reality. New and alternativesolutions are desired to allow a system to identify the occurrence ofdegradation and to isolate faults with greater accuracy and precision.

OVERVIEW

The present inventors have recognized, among other things, that aproblem to be solved is the need for new and/or alternative approachesto health degradation estimation and fault isolation. A healthmonitoring system may have as a goal a determination of whether controlalgorithms are relying on component and/or system models that correspondto reality. As components age or deteriorate, the models can be updatedor retuned to account for changing performance. If a component fails,the system model can be reconfigured to accommodate continued operationwith updated control metrics. The aim in some examples herein is toidentify the occurrence of a change, isolate the source of a change, andto then apply a mitigation.

A first illustrative and non-limiting example takes the form of aconfigurable controller for controlling a physical plant havingassociated therewith a plurality of actuators for controlling operationof the physical plant and a plurality of sensors for observing aplurality of characteristics of the physical plant operations, theconfigurable controller comprising: a state observer configured tocapture the current state of the physical plant by communication withthe plurality of sensors; a feature calculator configured to receivecurrent state data from the state observer and calculate at least onefeature parameter reflecting a state of health of at least one ofcomponent of the physical plant; an optimizer configured to optimizebehavior of the physical plant using at least the actuators; a healthestimator configured to receive the at least one feature parameter fromthe feature calculator and, subject to one or more entering conditions,apply a change probability model to the at least one feature parameterto determine whether the at least one feature parameter has changed toan extent indicative of a fault or health related degradation and, ifso, to generate a change indicator; and a fault isolator configured toreceive the change indicator and identify which component of thephysical plant that is subject to a fault or health degradation, and togenerate a fault indicator indicating the identified fault.

Additionally or alternatively, the configurable controller may furthercomprise a mitigator configured to modify the optimizer responsive tothe fault indicator, wherein: the optimizer is configured to use aplurality of factors to optimize behavior of the physical plant, eachfactor associated with a weight, and, prior to the health estimatorgenerating the change indicator, the optimizer uses a first set ofweights; and the mitigator is configured to respond to the faultindicator by modifying or replacing the first set of weights to therebymodify the optimizer.

Additionally or alternatively, the optimizer is configured to use atleast first and second models to optimize behavior of the physicalplant, by: relying on the first model when the fault isolator fails togenerate the fault indicator; and relying on the second model when thefault isolator generates the fault indicator.

Additionally or alternatively, the configurable controller may furthercomprise a mitigator configured to modify the state observer responsiveto the fault indicator, wherein: the state observer is configured toapply one or more tuning parameters to monitor the current state of thephysical plant; and the mitigator is configured to respond to the faultindicator by modifying or replacing one or more of the tuning parametersused by the state observer.

Another illustrative, non-limiting example takes the form of a systemcomprising the above described configurable controller and a physicalplant comprising: an engine having an intake manifold and an exhaustmanifold, with a combustion chamber therebetween into which a fuelquantity is provided; an airflow system coupled to the engine, theairflow system having a turbocharger having a compressor upstream of theintake manifold and a turbine downstream of the exhaust manifold, and anexhaust gas recirculation (EGR) valve configured to recirculate exhaustair from the exhaust manifold back to the intake manifold; and anaftertreatment system coupled to the airflow system downstream of theturbine; wherein the at least one feature parameter comprises: a firstfeature parameter related to mass air flow; a second feature parameterrelated to exhaust manifold air pressure; and a third feature parameterrelated to pressure downstream of the turbine.

Additionally or alternatively, the fault isolator is configured todetermine which component of the physical plant is subject to fault byanalysis of which of the at least one feature parameters has changed.

Additionally or alternatively, the fault isolator is configured to:determine the fault is with the EGR valve in response to finding thateither or both of the first or second feature parameters have changed;or determine the fault is with the aftertreatment if the third featureparameter has changed.

Another illustrative, non-limiting example takes the form of a systemcomprising the above described configurable controller and a physicalplant comprising: an engine having an intake manifold and an exhaustmanifold, with a combustion chamber therebetween into which a fuelquantity is provided; an airflow system coupled to the engine, theairflow system having a turbocharger with a compressor and a twin scrollturbine, an exhaust gas recirculation (EGR) valve, a first exhaustairpath from the exhaust manifold to a first scroll of the turbine, asecond exhaust airpath from the exhaust manifold to a second scroll ofthe turbine and a balance valve controllably allowing airflow betweenthe first and second exhaust airpaths; and an aftertreatment systemcoupled to the airflow system downstream of the turbine; wherein the atleast one feature parameter comprises: a first feature parameter relatedto mass air flow; a second feature parameter related to exhaust manifoldair pressure; and a third feature parameter related to pressuredownstream of the turbine

Additionally or alternatively, the fault isolator is configured todetermine which component of the physical plant is subject to fault byanalysis of which of the at least one feature parameters has changed.

Additionally or alternatively, the fault isolator is configured to:identify an aftertreatment fault if the third feature parameter haschanged; identify an EGR fault if both the first and second featureparameters have changed; or identify a balance valve fault if the secondfeature parameter has changed but the first feature parameter has notchanged.

Another illustrative, non-limiting example takes the form of an enginesystem comprising: an engine having an intake manifold and an exhaustmanifold, with a combustion chamber therebetween into which a fuelquantity is provided; an airflow system coupled to the engine, theairflow system having a turbocharger including a compressor and aturbine, and an exhaust gas recirculation (EGR) valve; an aftertreatmentsystem including a Lambda sensor; and a configurable controller forcontrolling the engine, the airflow system, and the aftertreatmentsystem, comprising: a state observer configured to capture the currentstate of the engine, airflow system, and aftertreatment system; afeature calculator configured to calculate a first feature parameterrelated to mass air flow, a second feature parameter related to exhaustmanifold air pressure, and a third feature parameter related to pressuredownstream of the turbine using data received from the state observer;an optimizer configured to optimize behavior of the engine, airflowsystem, and aftertreatment system using the current state of the engineand at least one model or setpoint; a health estimator configured toreceive the first, second and third feature parameters and, subject toone or more entering conditions, apply a change probability model to thefirst, second, and third feature parameters to determine whether any ofthe first, second or third feature parameters has changed to an extentindicative of a fault and, if so, to generate a change indicator; and afault isolator configured to receive the change indicator and identify afault by identifying a system component that is subject to a fault asindicated by the change indicator, and generate a fault indicatorindicating the identified fault.

Additionally or alternatively, the fault isolator is configured to:identify an aftertreatment fault if only the third feature parameter haschanged; or identify an EGR fault if either one, or both of, the firstand second feature parameters have changed.

Additionally or alternatively, the optimizer is configured to use eachof: a first model related to aftertreatment airflow; and a second modelrelated to EGR flow; and the system further comprises a health mitigatorconfigured to modify operation of the optimizer to: replace or reducereliance on the first model responsive to an identified aftertreatmentfault; and replace or reduce reliance on the second model responsive toan identified EGR fault.

Additionally or alternatively, the engine system may further comprise anexhaust manifold pressure sensor, wherein: the optimizer comprises oneor more models reliant on a pressure sensed by the exhaust manifoldpressure sensor; and the health mitigator is configured, responsive to afault, to increase reliance by the optimizer on the one or more modelsreliant on the pressure sensed by the exhaust manifold pressure sensor.

Additionally or alternatively, the engine system may further comprise aturbocharger speed sensor, wherein: the optimizer comprises one or moremodels reliant on a speed sensed by the turbocharger speed sensor; andthe health mitigator is configured, responsive to a fault, to increasereliance by the optimizer on the one or more models reliant on the speedsensed by the turbocharger speed sensor.

Another illustrative, non-limiting example takes the form of an enginesystem comprising: an engine having an intake manifold and an exhaustmanifold, with a combustion chamber therebetween into which a fuelquantity is provided; an airflow system coupled to the engine, theairflow system having a turbocharger having a compressor and a twinscroll turbine, an exhaust gas recirculation (EGR) valve, a firstexhaust airpath from the exhaust manifold to a first scroll of theturbine, a second exhaust airpath from the exhaust manifold to a secondscroll of the turbine and a balance valve controllably allowing airflowbetween the first and second exhaust airpaths; an aftertreatment systemincluding a Lambda sensor; and a configurable controller for controllingthe engine, the airflow system, and the aftertreatment system,comprising: a state observer configured to capture the current state ofthe engine, airflow system, and aftertreatment system; a featurecalculator configured to calculate a first feature parameter related tomass air flow, a second feature parameter related to exhaust manifoldair pressure, and a third feature parameter related to pressuredownstream of the turbine using data obtained from the state observer;an optimizer configured to optimize behavior of the engine, airflowsystem, and aftertreatment system based on the current state of theengine and at least one model or setpoint; a health estimator configuredto receive the first, second and third feature parameters and, subjectto one or more entering conditions, apply a change probability model tothe first, second, and third feature parameters to determine whether anyof the first, second or third feature parameters has changed to anextent indicative of a fault and, if so, to generate a change indicator;and a fault isolator configured to receive the change indicator andidentify a fault by identifying a system component that is subject to afault and generate a fault indicator indicating the identified fault.

Additionally or alternatively, the fault isolator is configured to:identify an aftertreatment fault if the third feature parameter haschanged; identify an EGR fault if both the first and second featureparameters have changed; or identify a balance valve fault if the secondfeature parameter has changed but the second feature parameter has notchanged.

Additionally or alternatively, the optimizer is configured to use eachof: a first model related to aftertreatment airflow; a second modelrelated to EGR flow; and a third model related to balance valve flow;and the system further comprises a health mitigator configured to modifyoperation of the optimizer to: replace or reduce reliance on the firstmodel responsive to an identified aftertreatment fault; replace orreduce reliance on the second model responsive to an identified EGRfault; replace or reduce reliance on the third model responsive to anidentified balance valve fault.

Additionally or alternatively, the engine system may further comprise anexhaust manifold pressure sensor, wherein: the optimizer comprises oneor more models reliant on a pressure sensed by the exhaust manifoldpressure sensor; and the health mitigator is configured, responsive to afault, to increase reliance by the optimizer on the one or more modelsreliant on the pressure sensed by the exhaust manifold pressure sensor.

Additionally or alternatively, the engine system may further comprise aturbocharger speed sensor, wherein: the optimizer comprises one or moremodels reliant on a speed sensed by the turbocharger speed sensor; andthe health mitigator is configured, responsive to a fault, to increasereliance by the optimizer on the one or more models reliant on the speedsensed by the turbocharger speed sensor.

This overview is intended to introduce subject matter of the presentpatent application. It is not intended to provide an exclusive orexhaustive explanation. The detailed description is included to providefurther information about the present patent application.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. The drawings illustrate generally, by way of example, butnot by way of limitation, various embodiments discussed in the presentdocument.

FIG. 1 shows an illustrative engine with a turbocharger;

FIG. 2 shows an illustrative engine with a twin scroll turbine;

FIG. 3 shows an illustrative example of a system responsive to a fault;

FIG. 4 shows an illustrative control system and physical plant;

FIG. 5 shows in block form an illustrative health monitor;

FIGS. 6A-6B illustrate an example health monitor and method; and

FIGS. 7-9 show illustrative Expert Tables for use in a health monitor.

DETAILED DESCRIPTION

FIGS. 1 and 2 each show illustrative engine systems on which the presentinvention may be operated. These examples are shown to aid in thepresentation of the operational steps, calculations, and solutions thatfollow. Other engine systems may be used instead, with differentconfigurations and components as desired.

FIG. 1 shows an illustrative engine with a turbocharger. The system 10includes an engine 20 having one or more cylinders 22, which receivesfuel from a fuel system 24, such as by one or more fuel injectors. Thefuel system 24 provides a known or set quantity of fuel for each firingof each cylinder 22, making for a determined quantity of fuel for eachfull firing sequence of the engine’s cylinders. The speed of the engine,N_(E), represents the speed at which a full firing sequence takes place(whether or not all cylinders are active).

The airflow of the system includes fresh air intake passing through anair filter 30 and then going to a turbocharger 40 having a compressor 42and turbine 44 linked together by a drive shaft. Air exiting thecompressor 42 is considered the “charged” air flow, having beencompressed, and it passes through a charge air cooler 50 to reducetemperature, and then to a throttle 52. To prevent turbocharger surge(reverse airflow through the compressor 42 which can be caused bypressure imbalances responsive to closing of the throttle 52), arecirculation valve 54 is provided to allow charged air to recirculateback to the compressor 42 input.

After passing through the throttle 52, the charged fresh air reaches theintake manifold of the engine 20, where combustion takes place. Prior toentering the intake manifold the charged fresh air mixes withrecirculated exhaust gasses that pass through an exhaust gasrecirculation (EGR) valve 60, which will typically also pass therecirculated gas through an EGR Cooler 62. The recirculated exhaust gasaids in reducing combustion temperatures in the engine 20, reducingcertain noxious emissions.

During combustion the charged air mass is combined with the fuel mass,m_(F). Exhaust gasses leave the engine at an exhaust manifold. Exhaustgasses are then directed to the turbine 44, which obtains torque/forcefrom the exhaust gas that is in turn applied via the drive shaft to thecompressor 42. In some examples the speed of rotation of theturbocharger drive shaft is a measured variable, referred to asturbocharger speed.

A wastegate 46 allows venting of exhaust gas without passing through theturbine 44 to control turbocharger speed. Rather than a wastegate 46, avariable geometry turbine (VGT) may be used to manage gasses enteringthe turbine 44, if desired. Gasses exiting the system either via theturbine 44 or the wastegate 46 go to an exhaust structure 70 wherevarious aftertreatment devices may remove or reduce pollutants. One ormore lambda sensors or universal exhaust gas oxygen (UEGO) sensors areprovided in the exhaust structure 70. The measured oxygen concentrationcan be used to determine air to fuel ratio in the engine 20, forexample.

Small boxes are shown throughout the figure representing temperature andpressure at various locations:

-   T₀, and p₀ represent the ambient air temperature and air pressure-   T₁, and p₁ represent pressure and temperature at the outlet of the    compressor 42;-   T₂, and p₂ represent pressure and temperature at the intake    manifold;-   T₃, and p₃ represent pressure and temperature at the exhaust    manifold; and-   T₄, and p₄ represent pressure and temperature at the outlet of the    turbine 44.

In a production system, the ambient air temperature and pressure (T₀,and p₀), and the intake manifold pressure and temperature (T₂ and p₂)may be measured parameters, and other pressures and temperatures areestimated, calculated and or inferred using a model of the system andother characteristics. Engine speed (N_(E)) will also be known, as isthe mass input via the fuel injectors of the engine 20, and the outputof the lambda sensor at the exhaust structure 70. In some examples, p₃may also be directly measured, and/or the turbocharger speed may bemeasured.

In a system under test, such as one used for modelling the system or onebeing tested at a test stand, additional sensors and pressures may becaptured throughout the system. The added sensors aid in the developmentand calibration of models used in production systems to estimate,calculate or infer the various pressures, temperatures, mass flows, etc.as needed.

The operation in general is controlled by an engine control unit (ECU)80. The ECU may include a microcontroller or microprocessor, as desired,or other logic/memory, application specific integrated circuit (ASIC),etc., with associated memory for storing observed characteristics aswell as operational instruction sets in a non-transitory medium, such asa Flash or other memory circuitry. The ECU 80 will be coupled to variousactuators throughout the system, as well as to the provided sensors, toobtain data and issue control signals as needed. The ECU may couple toother vehicle control systems such as by a controller area network (CAN)bus or other wired or wireless link.

FIG. 1 illustrates an example which is more or less typical for a dieselengine configuration, with the EGR 60 in a “high pressure” location,recirculating exhaust gasses from the exhaust manifold to the intakemanifold of the engine 20. Other fuels and configurations may be usedinstead. For example, a gasoline engine may use a low pressure EGR 60,which would link the output of the turbine 44 to the input of thecompressor 42 using a low pressure EGR valve, or even using a three-wayvalve taking as inputs the incoming air from the air filter 30 as wellas filtered and cooled recirculated exhaust gasses exiting the turbine44, with an output entering the compressor 42. Additional elements maybe present, such as by including an electric motor (E-Turbo) thatapplies added force as commanded to the drive shaft of the turbocharger40, and one or more features can be omitted or swapped out, such as byreplacing the RCV with a blow-off valve that vents charged air to theatmosphere rather than recirculating it, or any other suitable changes.

FIG. 2 shows an illustrative engine with a twin scroll turbine. One ofthe known tradeoffs with a standard turbocharger setup as shown in FIG.1 is that of low-speed responsiveness and high-speed efficiency. Whenthe engine operates at low speeds, the quantity of exhaust gas generatedmay drive the turbocharger turbine at relatively low speed itself. Acommand to increase engine speed will cause more exhaust gas to flow,however, the rotational inertia of the turbocharger system componentswill delay the turbocharger’s response. The tradeoff is that one canimprove responsiveness using a smaller turbocharger to reduce rotationalinertia, but that change can reduce efficiency at high speeds. Atwin-scroll turbocharger splits the exhaust gasses and applies a portionof exhaust gasses to a first part of the turbine, and another portion ofthe exhaust gasses to a different part of the turbine, improvingresponsiveness at low speeds without sacrificing efficiency at highspeeds.

In FIG. 2 , air flow is shown coming into the system at 100 (afterexiting a compressor, and charge air cooler, as desired), combining withEGR flow and entering the intake manifold for a six-cylinder engine 110(any number of cylinders may be present; typically, an even number suchas 4, 6, or 8). The system components as shown are controlled by one ormore ECUs 102. At the exhaust manifold, the exhaust air is split, withsome of the cylinder outputs ganged together at 114 and routed via afirst exhaust path 120, and the remaining cylinder outputs gangedtogether at 116 and routed via a second exhaust path 122. The enginereceives fuel from a fuel system 124 as before, providing a fuel mass,m_(F). The first exhaust path 120 is directed to a first portion of thevanes of the turbine 130, and the second exhaust path 122 is directed toa second portion of the vanes of the turbine 130, as illustrated. Abalance valve 150 is provided between the two exhaust paths 120, 122,and may be opened and/or closed as needed to balance the exhaust gasflow and/or pressure. Exhaust gasses can then exit the turbine 130, tothe aftertreatment 160, as illustrated.

In this example, an EGR valve (high pressure) is shown at 140,recirculating exhaust gasses to mix with the air flow 100. The EGR valve140 links to the first exhaust path 120, as shown. The balance valve 150can be used to equalize pressure as needed meaning that not all of therecirculated exhaust would necessarily come from the first exhaust path120.

The following variables are noted on FIG. 2 :

-   T₀, and p₀ represent the ambient air temperature and air pressure;-   T₁, and p₁ represent pressure and temperature at the outlet of the    compressor (not shown);-   T₂, and p₂ represent pressure and temperature at the intake    manifold;-   T_(3a), and p_(3a) represent pressure and temperature at the exhaust    manifold within the first exhaust path 120; and-   T_(3b), and p_(3b) represent pressure and temperature at the exhaust    manifold within the second exhaust path 122; and-   T₄, and p₄ represent pressure and temperature at the outlet of the    turbine 44.

Lambda sensor output and engine speed N_(E) are also availablevariables. The twin scroll configuration incorporates additional massflow variables (as compared to that of FIG. 1 ) by spitting the exhaustgas mass flow into two parts, and adding a balance mass flow throughvalve 150 to the overall analysis/system.

With either of the configurations in FIGS. 1 and 2 , EGR mass flow canbe measured using, for example, a Venturi-type sensor or an obstructiontype flow meter. EGR mass flow could instead by computed by sensing thepressure at the exhaust manifold of the engine and, knowing the pressuredifferential between the exhaust manifold and the intake manifold, massflow through the EGR can be calculated using a flow model for the EGRitself. Either sensor type and location (EGR flow sensor/meter, orexhaust manifold pressure sensor) increases system expense andcomplexity, and the added sensors are subject to failure in the hightemperature, dirty environment. Using the exhaust manifold pressure isfurther complicated due to the inclusion of separate exhaust paths 120,122 and balance valve 150.

The charge flow, that is the mass entering the intake manifold of theengine, is equal to the air flow (from the compressor) plus the EGR massflow. The charge flow can be additionally characterized using the burnedgas fraction, BGF, which is a function of the air flow, EGR mass flow,and injected fuel mass. BGF has a strong correlation to engine-outoxides of nitrogen (NOx) and is an important control variable.

In each of FIGS. 1 and 2 , the EGR mass flow can be estimated and/orcalculated without necessarily measuring mass flow through the EGR. Forexample, a virtual sensor model can infer total air flow throughout thesystem, with EGR flow then being the difference between air flowentering/exiting the compressor and charge flow into the intakemanifold. A lambda sensor in the exhaust airstream, combined withknowledge of the injected fuel quantity, can be used to infer the EGRmass flow after solving for charge flow and air flow. With leancombustion, however, the lambda sensor is generally less accurate thanwould be desired, and the data provided by a lambda sensor is subject todelay. A virtual sensor model may be inaccurate when pressure ratios arelow, due to reliance on pressure differences from one location toanother in the system for calculating flow rates.

In some illustrative examples, the EGR mass flow is determined using aweighted combination of a virtual sensor and the lambda sensor. Theweighted combination may use weighting coefficients that are dynamicallymodified in response to engine conditions. For example, with leancombustion and/or in transient conditions, weighting coefficients can beselected to favor the virtual sensor model. When pressure ratioscalculated by the virtual sensor are low, the weighting coefficients canbe selected to favor use of the lambda sensor. For example, when onefactor or the other is “favored,” the weighting coefficients can beselected so that the favored factor is the predominant input (greaterthan 50%, 75%, or 90% for example) of the resultant calculation.

The mass flows in the illustrative system of FIG. 2 can be modelledusing a limited set of equations. Each variable/model can be formulatedusing a multivariate polynomial of the form:

$\begin{matrix}{P( {u_{1},u_{2}} )\mspace{6mu} = \mspace{6mu} a_{0}\mspace{6mu} + \mspace{6mu} a_{1}\mspace{6mu} \cdot \mspace{6mu} u_{1}\mspace{6mu} + \mspace{6mu} a_{2}\mspace{6mu} \cdot \mspace{6mu} u_{2}\mspace{6mu} + \mspace{6mu} a_{3}\mspace{6mu} \cdot \, u_{1}^{2}\mspace{6mu} + \mspace{6mu} a_{4}\mspace{6mu} \cdot \mspace{6mu} u_{1}\mspace{6mu} \cdot \mspace{6mu} u_{2} + a_{4}\mspace{6mu} \cdot \mspace{6mu} u_{2}^{2}} & \text{­­­\{1\}}\end{matrix}$

Third or more variables may be included, with expansion of similarstructure. The models may also include or use a set of rationalfunctions of a form:

$\begin{matrix}{R( {u_{1},\mspace{6mu} u_{2}} )\mspace{6mu} = \mspace{6mu}\frac{N( {u_{1},\mspace{6mu} u_{2}} )}{D( {u_{1},\mspace{6mu} u_{2}} )}} & \text{­­­\{2\}}\end{matrix}$

Where N(u₁, u₂) and D(u₁, u₂) are each polynomials of the form shown inEq. 1. By generating each model as a multivariate and/or rationalfunction as shown in Eq. 1 and Eq. 2, the underlying derivations andcalculations will remain rational and can be accurately approximatedwith unbounded sensitivities. The analytical framework allows fastevaluation of the models and sensitivities. Within such models, errorfunctions can be set to detect and handle any circumstance in which thedenominators near zero.

The mass flows for a twin scroll design, as in FIG. 2 , can becalculated using the following equations. The aftertreatment (AFT) massflow is as shown in Equation 3:

$\begin{matrix}{m_{T1}( {p_{3a},p_{4},T_{3a}} )\mspace{6mu} + \mspace{6mu} m_{T2}( {p_{3b},p_{4},T_{3b}} )\mspace{6mu} = \mspace{6mu} m_{AFT}( {p_{4},p_{0},T_{4}} )} & \text{­­­\{3\}}\end{matrix}$

Where m_(T1) and m_(T2) represent mass flow into each scroll of theturbine, and m_(AFT) is the exhaust/aftertreatment mass flow. The flowbalance for Scroll 1 is as shown in Equation 4:

$\begin{matrix}\begin{array}{l}{m_{T1}( {p_{3a},p_{4},T_{3a}} )\mspace{6mu} + \mspace{6mu} m_{EGR}( {p_{3a},\mspace{6mu} p_{2},\mspace{6mu} T_{3a},u_{EGR}} )\mspace{6mu} + \mspace{6mu}} \\{m_{Bal}( {p_{3a},p_{3b},T_{3a},u_{Bal}} )\mspace{6mu} = \mspace{6mu}\frac{1}{2}( {m_{Ch}( {p_{2},m_{f},T_{2},N_{e}} )\mspace{6mu} + \mspace{6mu} m_{f}} )}\end{array} & \text{­­­\{4\}}\end{matrix}$

Where m_(EGR) is the EGR mass flow, m_(Bal) is the mass flow through thebalance valve, m_(Ch) is the charge mass air flow, and m_(F) is the fuelmass. The flow balance for Scroll 2 is shown by Equation 5:

$\begin{matrix}\begin{matrix}{m_{T2}( {p_{3b},p_{4},T_{3b}} )\mspace{6mu} - \mspace{6mu} m_{Bal}( {p_{3a},p_{3b},T_{3a},u_{Bal}} )\mspace{6mu} = \mspace{6mu}} \\{\frac{1}{2}\mspace{6mu}( {m_{Ch}( {p_{2},m_{f},T_{2},N_{e}} )\mspace{6mu} + \mspace{6mu} m_{F}} )}\end{matrix} & \text{­­­\{5\}}\end{matrix}$

The pressures p_(3a), p_(3b), and p₄ may be found by an iterativesolver, such as a Gauss-Newton, Newton-Raphson, or iterated KalmanFilter. An exhaust manifold pressure sensor is optional in theseequations. When the exhaust manifold pressure sensor is present,Equation 6 can be used as further discussed below in mitigation of oneor more failure modes:

$\begin{matrix}{p_{3a}\mspace{6mu} = \mspace{6mu} p_{3a\_ sensor}} & \text{­­­\{6\}}\end{matrix}$

Because the system state can be solved without the exhaust manifoldsensor, the inclusion of such a sensor can be used for the mitigationsdescribed below.

The total mass of incoming air flow (m_(A)) into the system can bedetermined using Equation 7:

$\begin{matrix}{m_{A}\mspace{6mu} = \mspace{6mu} m_{T1}( {p_{3a},p_{4},T_{3a}} )\mspace{6mu} + \mspace{6mu} m_{T2}( {p_{3b},p_{4},T_{3b}} ) - m_{F}} & \text{­­­\{7\}}\end{matrix}$

Or, alternatively as shown in Equation 8:

$\begin{matrix}{m_{A}\mspace{6mu} = \mspace{6mu} m_{AFT}( {p_{4},p_{0},T_{3a}} )\mspace{6mu} - \mspace{6mu} m_{F}} & \text{­­­\{8\}}\end{matrix}$

And the EGR mass flow would then be as shown in Equation 9:

$\begin{matrix}{m_{EGR}\mspace{6mu} = \mspace{6mu} m_{Ch}( {p_{2},m_{F},T_{2},N_{e}} )\mspace{6mu} - \mspace{6mu} m_{A}} & \text{­­­\{9\}}\end{matrix}$

The equations are somewhat simpler with a single scroll turbine. Theturbine outlet flow balance can be understood from Equation 10:

$\begin{matrix}{m_{T}( {p_{3},p_{4},T_{3},u_{T}} )\mspace{6mu} = \mspace{6mu} m_{AFT}( {p_{0},p_{4},T_{4}} )} & \text{­­­\{10\}}\end{matrix}$

Where u_(T), now included in the equation, is a turbine flow controllingactuator position, such as the wastegate or a variable geometry turbine(VGT). The mass flow balance can be understood from Equation 11:

$\begin{matrix}\begin{matrix}{m_{T}( {p_{3},p_{4},T_{3},u_{T}} )\mspace{6mu} + \mspace{6mu} m_{EGR}( {p_{2},p_{3},T_{3},u_{EGR}} )\mspace{6mu} = \mspace{6mu} m_{Ch}( {p_{2},m_{F},T_{2},N_{e}} ) +} \\m_{F}\end{matrix} & \text{­­­\{11\}}\end{matrix}$

If desired, the charge mass may also be calculated with the inclusion ofmeasured p₃ (to the extent it is available) as one of the parameters.Again, pressure p₃ and p₄ are found by an iterative solver, such as aGauss-Newton, Newton-Raphson, or iterated Kalman Filter. The exhaustmanifold pressure sensor is optional in these equations. Inclusion ofthe p₃ sensor can be used for fault identification and/or mitigation,using Equation 12:

$\begin{matrix}{p_{3}\mspace{6mu} = \mspace{6mu} p_{3\_ sensor}} & \text{­­­\{12\}}\end{matrix}$

The incoming air flow can be calculated for the single scroll turbineusing Equation 13 or 14:

$\begin{matrix}{m_{A}\mspace{6mu} = \mspace{6mu} m_{T}( {p_{3},p_{4},T_{3},u_{Trb}} )\mspace{6mu} - \mspace{6mu} m_{F}} & \text{­­­\{13\}}\end{matrix}$

Or, alternatively:

$\begin{matrix}{m_{A}\mspace{6mu} = \mspace{6mu} m_{AFT}\mspace{6mu}( {p_{4},p_{0},T_{3}} )\mspace{6mu} - \mspace{6mu} m_{F}} & \text{­­­\{14\}}\end{matrix}$

And the EGR mass flow is again the same as in Equation 9, above.

The data extraction for the twin-scroll turbine model, and particularlywith reference to the balance valve flow, will depend on accuracy ofturbine flow mapping, EGR valve modelling, and the charge flow model.With that many inputs, the data output can be uncertain. In someexamples, the exhaust pressure measurement (if available) can be used todirectly estimate the mass flow through the balance valve. In otherexamples, assuming again that exhaust pressure measurement is available,the turbine mass fraction flow for each scroll may instead be estimated.Using the exhaust pressure measurement either way will reduce theuncertainty of the model, specifically that of the balance mass flow,m_(Bal)(p_(3a), p_(3b), T_(3a), u_(Bal)). This substitution may beparticularly useful when a fault is identified. More particularly, if afault is identified with the balance valve, models relying on thebalance valve become unreliable, and a mitigation can be to switch to amodified analysis that omits the balance valve model, which bothequations 4 and 5 above rely upon, as discussed below.

For example, mass balance equations can be used to resolve the mass flowas follows. The turbine outlet flow balance can be expressed as:

$\begin{matrix}{m_{T1}( {p_{3a},p_{4},T_{3a}} )\mspace{6mu} + \mspace{6mu} m_{T2}( {p_{3b},p_{4},T_{3b}} )\mspace{6mu} - \mspace{6mu} m_{AFT}( {p_{4},p_{0},T_{4}} ) = 0} & \text{­­­\{15\}}\end{matrix}$

The flow balance on scroll 1 can expressed as:

$\begin{matrix}\begin{matrix}{m_{T1}( {p_{3a},p_{4},T_{3a}} )\mspace{6mu} + \mspace{6mu} m_{EGR}( {p_{3a},p_{2},T_{3a},u_{EGR}} )\mspace{6mu} + \mspace{6mu} m_{Bal} -} \\{\frac{1}{2}( {m_{Ch}( {p_{2},m_{F},T_{2},N_{e}} )\mspace{6mu} + \mspace{6mu} m_{F}} )\mspace{6mu} = \mspace{6mu} 0}\end{matrix} & \text{­­­\{16\}}\end{matrix}$

The flow balance on scroll 2 can be expressed as:

$\begin{matrix}{m_{T2}( {p_{3b},p_{4},T_{3b}} )\mspace{6mu} - \mspace{6mu} m_{Bal}\mspace{6mu} - \mspace{6mu}\frac{1}{2}( {m_{Ch}( {p_{2},m_{F},T_{2},N_{e}} )\mspace{6mu} + \mspace{6mu} m_{F}} )\mspace{6mu} = \mspace{6mu} 0} & \text{­­­\{17\}}\end{matrix}$

In Equations 16 and 17, m_(Bal) is treated as a freely estimated signal,rather than being modelled as above in Equations 4 and 5. Pressuresensor information can be expressed as:

$\begin{matrix}{p_{3a}\mspace{6mu} = \mspace{6mu} p_{3a\_ sensor}} & \text{­­­\{18\}}\end{matrix}$

The augmented vector can then be expressed as p_(3α),p_(3b),p₄,m_(Bal).

As an alternative, the system can start with the turbine outlet andaftertreatment (AFT) flow balance:

$\begin{matrix}{m_{T}( {p_{3a},p_{3b},p_{4},T_{3a},T_{3b},T_{4}} )\mspace{6mu} - \mspace{6mu} m_{AFT}( {p_{4},p_{0},T_{4}} )\mspace{6mu} = \mspace{6mu} 0} & \text{­­­\{19\}}\end{matrix}$

The balance on scroll 1 is then:

$\begin{matrix}\begin{matrix}{( {1 - \beta} )\mspace{6mu} \ast \mspace{6mu} m_{T}( {p_{3a},p_{3b},p_{4},T_{3a},T_{3b},T_{4}} )\mspace{6mu} + \mspace{6mu} m_{EGR}( {p_{3a},p_{2},T_{3a},u_{EGR}} ) -} \\{\frac{1}{2}( {m_{Ch}( {p_{2},m_{F},T_{2},N_{E}} )\mspace{6mu} + \mspace{6mu} m_{F}} )\mspace{6mu} = \mspace{6mu} 0}\end{matrix} & \text{­­­\{20\}}\end{matrix}$

Where β indicates the fraction of the total exhaust passing throughscroll 1. As represented in Equation 20, the mass flow through scroll 1,plus the EGR mass flow, equals one half of the mass exiting the exhaustmanifold of the engine, and is adjusted for balance valve flow using β.The scroll 2 flow balance is shown in Equation 21:

$\begin{matrix}\begin{matrix}{\beta\mspace{6mu} \ast \mspace{6mu} m_{T}( {p_{3a},p_{3b},p_{4},T_{3a},T_{3b},T_{4}} )\mspace{6mu} - \mspace{6mu}\frac{1}{2}( {m_{Ch}( {p_{2},m_{F},T_{2},N_{E}} )\mspace{6mu} + \mspace{6mu} m_{F}} )} \\{= 0}\end{matrix} & \text{­­­\{21\}}\end{matrix}$

The resulting augmented vector can be expressed as p_(3α),p_(3b),p₄, β.Equations 6 and 7 can be used again to characterize the mass balance;optionally, Equation 7 can be consolidated to reduce the turbine massflow to a single function expressed asm_(T)(p_(3a),p_(3b),p₄,T_(3a),T_(3b),T₄) if desired. The EGR mass flowequation can then be modified to use β as shown in Equation 22:

$\begin{matrix}\begin{matrix}{m_{EGR}\mspace{6mu} = \mspace{6mu}\frac{1}{2}( {m_{Ch}( {p_{2},m_{f},T_{2},N_{e}} )\mspace{6mu} + \mspace{6mu} m_{F}} )\mspace{6mu} - \mspace{6mu}( {1 - \beta} )\mspace{6mu} \ast} \\( {m_{T}( {p_{3a},p_{3b},p_{4},T_{3a},T_{3b},T_{4}} )} )\end{matrix} & \text{­­­\{22\}}\end{matrix}$

The set of equations and variables shown above illustrates the air flowmodel that can be iteratively solved on an ongoing basis to calculatevarious pressures, temperatures and air flow in an engine system. Theanalysis may be referred to as a virtual sensor insofar as severaltemperatures, pressures and air flow in the system are calculated,rather than being sensed or measured, with the output of thecalculations serving as a “virtual” sensor. Test stand operations andother suitable calibrations may be performed to create and/or updatecoefficients within the airflow model. For example, as components age orare replaced, the model may be rebuilt at a test station and/or themodel may be adjusted over time to accommodate aging. For example, asystem health monitor may be used to calculate model changes related tocomponent aging, such as by using a function of time, a function ofusage, or through measurement or virtual monitoring of changingperformance/operation of various components.

An air flow fusion is then used to calculate the air flow. There are twosources: air flow computed from the (filtered) equivalence ratio, whichrelies on the lambda sensor, and mass balance from the mass flow solvershown above. The lambda sensor is most accurate in steady state, andwhen the exhaust has a relatively low oxygen content (such as less than10% oxygen). On the other hand, the mass balance and flow solver areless accurate when the delta between p_(3a) and p_(3b) is small.

The lambda relationship is shown in Equation 23:

$\begin{matrix}{\lambda\mspace{6mu} = \mspace{6mu}\frac{1}{AFR_{stoich}} \cdot \frac{m_{A}^{\lambda}}{m_{F}}} & \text{­­­\{23\}}\end{matrix}$

As can be seen, the output of the lambda or air-fuel ratio (AFR) sensorwill be the mass of the air flow, divided by the mass of fuel, times theinverse of the stoichiometric ratio for the relevant fuel. RearrangingEquation 23, m_(A) ^(λ), equals the product of the lambda sensor output,fuel mass, and the stoichiometric ratio for the relevant fuel.

In some examples, air flow fusion can be calculated using a weightedaverage such as shown in Equation 24, where the value of m_(A) isdetermined such that the magnitude of the vector using the two weightingvalues is minimized:

$\begin{matrix}{\min\limits_{m_{A}}\| \begin{matrix}{w_{1} \cdot ( {m_{A}^{\lambda} - m_{A}} )} \\{w_{2}\mspace{6mu} \cdot \mspace{6mu}( {m_{A}^{T,EGR,Aft} - m_{A}} )}\end{matrix} \|^{2}} & \text{­­­\{24\}}\end{matrix}$

Where the two weights w₁ and w₂ are each functions of other variables.For example, w₁ may be function of the lambda sensor output, as byreducing it in linear fashion from a nominal value to zero as the oxygencontent goes from 10% to 5% oxygen (or other function through a similar,wider, or narrower range of oxygen values). If the lambda sensor statusis false (such as due to component failure or transient operation), w₁may be set to zero. The other weight, w₂, may be a function of thedifference between p_(3a) and p_(3b) as calculated in the pressure modelof the system; that is, w₂ may drop from a nominal value to zero over arange as the difference between p_(3a) and p_(3b) drops below a presetthreshold. These constraints on the weighting values may be omitted insome examples.

FIG. 3 shows an illustrative example of a fault mitigation approach. Anominal operating mode is illustrated at 160. A set of ECU data 162 isprovided to a collection of component and sub-system or system models at164. Component models can be generated for individual componentry of thesystem. An example may be, for example and without limitation, a modelof the flow characteristics in any of the system components such as thecompressor, air coolers, EGR valve, wastegate, turbine, and engine, aswell as associated linking components therebetween. Such models may bedetermined on an isolated basis for each component, or may be modeledfor subsystem and/or system operation. The preceding discussion ofvarious flow equations provides a detailed sequence and set of examples.The ECU data from 162 is provided to the models, resulting in a set ofequations that can be solved using suitable analytical tools asindicated at 166. Any of the above noted solvers and filters may be usedfor providing a multivariable analysis and non-linear (or linear, as thecase may be) analysis performed by the ECU and/or dedicated processorsand circuitry to calculate a set of outputs indicated at 170. Theillustrative outputs include the EGR valve mass flow 172, the mass airflow 174, and the burned fuel fraction 176.

Additional sensors 168 can be used to augment the analysis as describedabove where, for example, the normal operation models 164 and/or solvers166 are capable of generated the outputs at 170 without the “additional”sensors. An example from the above discussion is the exhaust manifoldpressure sensor, which may serve as the additional sensor 168, whichdoes not need to be used in the ordinary analysis. The additionalsensor(s) 168 can be used as a substitute for an identified faultycomponent when mitigating the fault.

In the illustrative example, a fault is then identified at indicated at178, and the system switches to using a different analysis, with thenewly implemented analysis 180 configured specifically to the fault 178.A fault may be identified using a bottom-up approach, looking directlyat individual actuators or components or at parameters affected by theindividual actuator or component. For example, a stuck or blocked EGRvalve can be identified by determining that the EGR valve fails toactuate responsive to a control signal, whether by observation of thevalve actuator itself, or by observing a failure of system parameters tochange in response to attempted EGR valve actuation. A blockedaftertreatment (AFT) may be identified due to failure of the AFT-relatedsensors to change values, or an out of range sensor output. A blocked orstuck balance valve may be identified similar to the EGR valve, that is,by directed observation of the actuator failing to respond to a controlsignal change, or by failure of system parameters to change when thebalance valve is actuated.

In some examples fault isolation is performed as a second part to abroader process that takes a more top-down approach. First, an analysisis performed to determine whether a fault has occurred and, if so, thenfault isolation is performed as shown in the analysis starting with FIG.4 , below.

Mitigation can be performed in various ways. For example, if the faultidentified is a blocked aftertreatment (AFT), a model relying on AFTsensing can be omitted. Using reference to the above numbered equations,Equations 3 (twin scroll turbine) or 10 (single scroll turbine) may beomitted from the set of equations and models used, as those equationsrely on the model for aftertreatment flow which is no longer reliabledue to the fault. The additional sensor value, such as the exhaustmanifold pressure, can be provided to the set of equations instead.

If the fault identified relates to the EGR being blocked or stuck, amodel relying on the EGR can be omitted or substituted. Using referenceto the above numbered equations, Equations 4 (twin scroll turbine) or 11(single scroll turbine) may be omitted. The additional sensor value,such as the exhaust manifold pressure, can be provided to the set ofequations instead.

If the balance valve of a twin scroll turbine implementation is blockedor stuck, an augmented formulation for computing the mass flows can beimplemented. With a twin scroll turbine, Equations 15-18 may beillustratively used and take the place of equations 3-5, with theexhaust manifold pressure, as sensed, incorporated into the analysis asp_(3a). Using p_(3a) allows calculation of the EGR mass flow, which inturn resolves the charge air flow into the engine. Including m_(Bal) asa freely estimated variable facilitates the analysis of Equations 15-18,having a set of four equations with four unknowns (p_(3a), p_(3b), p₄and m_(Bal)). Another approach is to use β to represent the fraction ofthe total exhaust passing through scroll 1, as shown in Equations 19-22,resulting in a set of four equations with four unknowns (p_(3a), p_(3b),p₄ and β).

In still another example with the balance valve blocked or stuck, eachof an exhaust manifold pressure (p_(3a)) and the intake manifoldpressure, p₂, may be used to calculate the air mass flow using equation25:

$\begin{matrix}{m_{A}\mspace{6mu} = \mspace{6mu} m_{Ch}( {p_{2},m_{f},T_{2},N_{e}} )\mspace{6mu} - \mspace{6mu} m_{EGR}( {p_{3a},p_{2},T_{3},u_{EGR}} )} & \text{­­­\{25\}}\end{matrix}$

Thus, here, the charge blow model and EGR valve model flow, both ofwhich can be fed by direct measurement of p_(3a) and p₂ are used tocharacterize the mass flow.

The mitigations described above are intended to be illustrative. Othermitigations may be used instead. In some embodiments, it is sufficientthat, responsive to an identified fault, one or more analytical modelsused to calculate airflow in the engine system is underweighted oromitted. Instead of the underweighted or omitted analytical model, asensed parameter that is not relied upon during the ordinary operationis integrated into the analysis. In some examples, the “not used” sensormay be used for fault identification, if desired.

In another alternative approach, rather than an exhaust manifoldpressure sensor, the additional sensor 186 may be the turbochargerspeed, which provides both turbine and compressor speeds. Using aturbocharger model and the torque balance on the turbocharger shaft, theanalysis can determine the charge air mass flow as well as the exhaustmass flow (taking into account the effect of VGT or WG operation), andthe iterative solver can again reduce the problem formulation to fourequations and four unknowns.

The newly implemented analysis integrates the “additional” sensor 186into the analysis, such as by changing the weighting applied to aplurality of models, or by replacing one model with another in theanalysis. ECU data 182 is provided to the modified set of component andsystem/subsystem models 184, which also uses the additional sensor data186. The resulting set of equations is submitted to the solvers andfilters at 188 to again provide the same set of outputs 170 as before.The result is that the response to the fault 178 provides the set ofsystem data 170 that allows continued operation of the system even afterthe fault is identified. It may be that the system operation isultimately limited in some sense, or less optimized than before, but thesystem in some examples remains operational within defined limits toavoid the need for complete shutdown due to, for example, violation ofemissions or other requirements. That is, with the set of output systemdata 170 still available after the fault 178, the overall control neededfor continued operation within operating limits remains possible untilthe fault 178 can be resolved in, for example, a subsequent maintenanceactivity.

The modified analysis can take several forms. In some examples, a set ofweighting values are used to determine which of the available analysesare predominant in the system state calculations. Rather than omittingan equation from the analysis, some examples swap new weighting valuesinto the analysis to minimize the impact of one or more models on theoutcome analysis. For example, with an Iterated Kalman Filter (IKF)solver, the tuning matrix or measurement covariance matrix can beadjusted to apply large gains to those models that are deemedunreliable, effectively reducing the impact of such models on theoutput. For other solvers, the weight values can be reduced instead. Theeffect can be to underweight or effectively omit, for purposes of thesolvers being used in a particular implementation, those models thatbecome unreliable due to the identified fault.

While the specific manner of implementing the change is dependent on thetype of solver used, the aim is to minimize the impact of a model which,due to an identified fault 178, is no longer reliable, on the analyticaloutputs 170. Rather than implementing a different solver, or loading anentirely new analysis matrix to the solver, swapping in a new set ofgains or weights responsive to the identified fault is used as themitigation in some examples. Other examples may switch to a differentsolver, or may reload a distinct set of equations to the solver in placeof those previously used, if desired, as mitigation for the fault 178.When the solver is switched, or the equations are swapped, the effectcan be to omit, for purpose of the solvers being used in a particularimplementation, those models that become unreliable due to theidentified fault.

If, at a later time, it is determined that the fault causing a model tobecome unreliable has ceased (i.e., a stuck valve becomes unstuck, or ablockage of a valve or other component is cleared), the system mayrevert to the original normal operation described above.

FIG. 4 illustrates a system and control configuration. A control systemoperates on an underlying system 200 comprising a set of actuators 202operating on a physical plant 204. The physical plant is observed usinga set of sensors 206.

A control apparatus as shown in this example includes a state observer220, which feeds a set of current state variables x(k) to an optimizer230. The optimizer 230 calculates a solution for process parameters thatcan be applied to a set of actuators 202, which in turn controloperation of the physical plant 204. The set of actuators 202 may applyto control, for example and without limitation, fuel or other injectors,variable nozzle turbine position, throttle valve, engine brake,after-treatment (including exhaust), exhaust gas recirculation (EGR),turbocharger, an electric motor (in an electric turbocharger forexample, which may be controlled via pulse width modulation (PWM)), thewaste gate (WG) actuator position (stroke), position of a recirculationvalve actuator, position of the variable compressor geometry actuator;and combinations thereof.

The physical plant 204 may be, for example and without limitation, aninternal combustion engine, whether diesel or gasoline, or a subsystemthereof, such as a turbocharger, the system airpath as a whole,catalysts, etc. Illustrative and non-limiting examples of a physicalplant 204 and associated actuators 202 and sensors 206 are shown abovein FIGS. 1 and 2 , for example. The present invention may also be usedmore broadly in other systems outside of the engine or vehicle context.

In some examples, sensors 206 may include, for example, and withoutlimitation, sensors detecting manifold absolute pressure (MAP), mass airflow (MAF), EGR flow, turbo speed, NOx, engine speed, fuel quantity,boost pressure, etc. Additional monitored parameters may include, forexample, torque output of the electric motor of an electricturbocharger, waste gate (WG) normalized opening, recirculation valve(RCV) normalized opening, and/or a variable geometry compressorconfiguration. Such sensors may be configured to sample the underlyingparameter being sensed and provide the result of such samples to thestate observer 220. The state observer 220 may record the underlyingsensed parameters, as well as actuator positions, over time to providehistory of the system operation.

The state observer 220 and optimizer 230 may be, for example,implemented in a microcontroller configured to operate on a set ofstored instructions for performing a state observation and optimizationroutine. In another example, an application specific integrated circuit(ASIC) may provide state observer functions, which can include thecapture or accumulation of data from the actuators 202 and/or sensors206, which in turn may be read periodically by a microcontrollerconfigured with stored instruction sets for performing a control and/oroptimization calculation using, for example, model predictive control(MPC) cost functions, linear quadratic regulator (LQR) control,proportional integral derivative (PID) control, or other controlalgorithms. The optimizer 230 may be integrated into, or providedseparately from, an on-board diagnostics system (not shown) that can beused to record diagnostic variables and present them, as needed to theuser or to store for later analysis, both of which may additionally beintegrated, if desired, into the overall vehicle processing unit.

The output of the optimizer 230 is used to control the actuators 202 tooperate the plant in a manner to minimize the distance of operatingparameters from one or more target output values for the controllableoutputs or physical plant operating characteristics. For example, thetargets may be any of target turbocharger speed, target boost pressure,target pressure difference over the compressor, target air mass flow ora combination thereof. For example, with MPC functions, the distance totarget or reference values for the one or more output values (orresulting operating characteristics) is minimized, thus optimizingperformance. As an example, a traditional MPC cost function formationmay be as shown in Equation 26:

$\begin{matrix}{J_{MPC}\mspace{6mu} = \mspace{6mu} min\mspace{6mu}{\sum_{k = 1}^{P}{\| {y_{r,k} - y_{k}} \|_{W_{1}}\mspace{6mu} + \mspace{6mu}\| {u_{d,k} - u_{k}} \|_{W_{2}},}}} & \text{­­­[Eq. 26]}\end{matrix}$

Where u_(d,k) corresponds to the desired profile for the manipulatedvariable, u_(k) stands for the manipulated variable, k denotes discretetime instance, and P stands for the prediction horizon of the predictivecontroller. In this example, y_(r,k) represents output reference values,and y_(k) represents predicted values provided according to mathematicalmodelling of the physical plant to be controlled during the relevanttime horizon, while and W₁ and W₂ specify weighting terms. Forsimplicity the k terms may be omitted in subsequent equations herein.The traditional MPC cost function is minimized in operation in order toprovide optimal control to the physical plant 204. Such a process may beperformed by the optimizer 230.

In another example, a PID controller can be used to account for each ofproportional, integral, and derivative differences from a targetoperating point. The proportional difference may indicate current state,integral difference may identify a process shift over time, andderivative difference may indicate the direction of changes inoperation. With PID control, a proportional difference is minimizedwhile monitoring to ensure that the integral and derivative differencesdo not indicate changing performance which may, after furtheriterations, cause the proportional difference to increase. The controlparameters output to the actuators 202 are, for a PID controller,adjusted to reduce or minimize the distance of actual performance fromone or more targets on an iterative basis. The optimizer 230 may use PIDcontrol instead of MPC, for example. LQR control may be used instead, ifdesired, applying similar concepts.

The system shown in FIG. 4 further comprises a health monitor 240,configured to receive information from the sensors 206, physical plant204, and/or actuators 202, as well as the state observer 220. Receivedinformation may, for example, reflect a current state (for example,whether and to what extent a valve is open), though such informationfrom the actuators may instead or in addition be obtained by monitoringcontrol signals from the optimizer 230 to the actuators 202, as desired.

Block 240 is shown for illustrative purposes but may be understood asbeing integrated into the optimizer 230. The operations of blocks 220,230, 240 may be implemented in a microcontroller configured to operateon a set of stored instructions for performing a state observation,health management and optimization routine. In another example, each ofblocks 220, 230, 240 may be performed by separate microcontrollers orapplication specific integrated circuits (ASIC).

The health monitor 240 is shown in FIG. 4 as including each of a faultor health degradation mitigator 242, and a health and fault analysisblock 244. In an example, the health and fault analysis block 244 usesreceived data to determine whether health degradation or component faultare occurring. For purposes of the following discussion, healthdegradation and component fault may each be considered a “fault” to theextent that either is identified as occurring according to a changeanalysis block further described below. The fault and health mitigationblock 242 responds to an output of block 244 to apply a mitigation tothe system. In some examples, a mitigation is applied by modifying theoperation of the optimizer. For example, weighting values or models usedin the optimizer may be modified, or data within models used by theoptimizer may be modified, as explained in US Pat. Application No.17/008,076, filed Aug. 31, 2020, and titled HEALTH CONSCIOUS CONTROLLER,as well as US Pat. Application No. 17/327,066, filed May 21, 2021, andtitled ENGINE MASS FLOW OBSERVER WITH FAULT MITIGATION, the disclosuresof which are incorporated herein by reference.

The aim overall in some examples is to determine whether the stateobserver and/or optimizer are performing with models that correspond toreality. If not, the accuracy of estimated states can be jeopardized,and the ability of an optimizer model to calculate an optimal solutionmay be impaired. For example, the state observer may have a health-basedreconfiguration functionality, in which observer tuning parameters canbe switched (i.e., a hard switch, replacing one model with anotherentirely or swapping out one set of tuning parameters for another), or acontinuous retuning can be performed responsive to gradient changes ofthe underlying component and/or system health. In an example, the stateobserver may rely on, for example and without limitation, a Kalmanfilter, and the associated measurement error covariance of the Kalmanfilter can be retuned and/or adjusted to account for changes in systemhealth or the presence of a fault. An optimizer may use multiple modelsand/or weighting values as well, which can be hard switched or subjectedto incremental changes over time in response to changes in componenthealth/performance. In still another example, an optimizer may beconfigured to use a plurality of different models and fuse outputs usinga weighting matrix; when a fault arises that makes one of the pluralityof models unreliable, the fusion or weighting matrix can be adjusted toeliminate or reduce the influence of the unreliable model.

FIG. 5 shows an illustrative health monitor 310 that may be used aselement 240 of FIG. 4 . The health and fault analysis block 244 of FIG.4 is expanded here to include two main components: a change analysisblock 320 and a fault identifier 340. In the example, the state observer300 (which may serve also as block 220 in FIG. 4 ) is used to capturethe current state of the controlled system, which may be an enginesystem. A feature calculator 302 generates a plurality of featureindicators 304 I₁, I₂, and I₃, based on the current state or storedhistorical states of the system as obtained from the state observer 300and/or direct sensor measurements. The feature indicators 304 areprovided to the change analysis block 320. While three featureindicators are shown in this example, any number (1 to dozens orhundreds) of feature indicators may be provided.

Each feature indicator 304 is processed, and the processed value islater compared to a corresponding threshold by the change analysis block320, subject to a set of entering conditions 330. A set of changeprobability models 322 is used to determine whether a change has takenplace, thereby triggering the fault identifier 340 and subsequentmitigation 350. In the illustrative example shown, the set of enteringconditions 330 is used to enable or disable change analysis for each ofthe change probability models and associated feature indicators 304.Entering conditions 330 may be provided to ensure that the analysis isperformed on useful data. Before explaining the entering conditions 330,a set of feature indicators 304 and associated change analysis 320 willbe detailed.

In an illustrative example, a set of feature indicators 304 may be asshown in Feature Set 1:

$\begin{matrix}\begin{matrix}{I_{1} = \frac{m_{A,EGRv} - m_{A,\lambda} - \mu_{\text{Δ}1}}{\sigma_{\text{Δ}1}}} \\{I_{2} = \frac{p_{3a}^{sens} - p_{3a} - \mu_{\text{Δ2}}}{\sigma_{\text{Δ2}}}} \\{I_{3} = \frac{p_{4,nom} - p_{4} - \mu_{\text{Δ3}}}{\sigma_{\text{Δ3}}}}\end{matrix} & \text{­­­\{Feature Set 1\}}\end{matrix}$

Wherein, for I₁, m_(A,ECRv) is the airflow computed as the charge flowminus the EGR valve flow as calculated using an EGR valve model, m_(A,λ)is the airflow computed using a model based on the lambda sensor output,µ_(Δ1) is a mean difference between the two airflows under nominalconditions, and σ_(Δ1) is the standard deviation of the differencebetween the two airflows under nominal conditions. For I₂, p_(3a)^(sens) is the measured pressure observed by an exhaust manifoldpressure sensor, p_(3a) is the solved exhaust pressure from the balanceflow (for a twin scroll turbine, in this example), and µ_(Δ2) is a meandifference between the two pressures under nominal conditions, andσ_(Δ2) is the standard deviation of the difference between the twopressures under nominal conditions. For I₃, p₄,_(nom) is a modeledpressure downstream of the turbine calibrated under nominal healthyconditions, p4 is the solved downstream turbine pressure from thebalance flow, µ_(Δ3) is a mean difference between the two pressuresunder nominal conditions, and σ_(Δ2) is the standard deviation of thedifference between the two pressures under nominal conditions.

Based on the preceding paragraph’s description of the featureparameters, the entering conditions may be as follows:

-   For a change probability model applicable to I₁, the entering    conditions may be, for example and without limitation, a set minimum    EGR mass flow (such as greater than 0.3 kg/s, for example and    without limitation), a fuel mass that is greater than zero, and that    the lambda sensor is ON / active.-   For the change probability model applicable to I₂, the entering    condition may be, for example, that the absolute value of the    difference between p_(3a) ^(sens) and p_(3a) is below a threshold    (such as, for example and without limitation, 20 kPa).-   For the change probability model applicable to I₃, the entering    condition may be, for example, that the absolute value of the    difference between p₄,_(nom) and p₄ is below a threshold (such as,    for example and without limitation, 2.5 kPa).

The numerical limitations noted are illustrative for one example; othervalues may apply to different implementations. As can be seen, theentering conditions prevent the change analysis from being performed inaberrant or unreliable conditions. For example, the change conditionswhen analyzing mass flow may not apply in the absence of a valid lambdasensor output (sensor off), or when no fuel flow or EGR flow is present(as each would make one of the underlying models unreliable). Likewise,highly transient conditions (as may cause large deltas in the pressurevalues) may be excluded from the analysis for I₂ and I₃. The enteringconditions block 330 provides an enabling or disabling signal to each ofthe individual change analysis blocks 322 depending on whether thecorresponding entering condition is met. The entering conditionsillustrated above are specific to one example, and different thresholds,analyses, or types of entering conditions may be used in otherimplementations.

Each change analysis block 322, in this example, generates a continuousprobability W₁, W₂, W₃ about the change of the corresponding featureinput I₁, I₂, I₃. The individual outputs W₁, W₂, W₃ may representprobabilities of selected conditions, which the fault identifier 340 canthen use to determine whether a particular component is in a fault orunhealthy condition. These continuous probabilities W₁, W₂, W₃ may bescaled or normalized to an interval of 0 and 1 in some examples; scalingor normalization may take place in any suitable fashion or may beomitted. In an alternative approach, each of W₁, W₂, W₃ may provide aBoolean indicator (0 or 1; True or False, for example) of a particularcondition. In an example, using the definitions for I₁, I₂, and I₃ inFeature Set 1, the outputs W₁, W₂, W₃ can indicate whether, for W₁, theairflow calculated using the EGR valve model is equal to the airflow ascalculated using a Lambda sensor output; W₂ can indicate whether thesensed exhaust manifold pressure is equal to a calculated/modeledexhaust manifold pressure, and W₃ can indicate whether a modeledpressure downstream of the turbine calibrated under nominal healthyconditions is equal to the solved downstream turbine pressure from thebalance flow (in a twin scroll turbocharger configuration).

Other features/conditions may be used, as desired, and any suitablenumber of features may be tested/applied. For example, additionalinequality conditions may determine whether a sensed turbocharger speedis equal to a modeled turbocharger speed, whether an airflow sensedusing an airflow sensor matches a modeled airflow, and/or whether apressure or temperature sensed anywhere in the system matches acounterpart modeled pressure or temperature, as desired. Further, whileFeature Set 1 shows three features, there may be any number of features,including 1, 2, 3, 4, 5, etc., without limitation. For example, FeatureSet 1 is configured for an implementation of a twin scroll turbochargerhaving an exhaust manifold pressure sensor available in the system. Inanother example, the twin scroll feature can be omitted, and the featureset can omit I₃ (which relies on a balance valve model that would not bepresent without the twin scroll turbine), and I₂ would simply refer top₃ ^(sens) and p₃, instead.

In some examples, the change analysis blocks 322 are embodied assoftware components, or as dedicated hardware components, that apply achange probability model. One suitable change probability model is aSequential Probability Ratio Test (SPRT). Others suitable changeprobability models may include, for example and without limitation, ageneralized likelihood ratio algorithm, a scaled sequential probabilityratio test, a truncated sequential probability ratio test, and/or anexact binomial test. In some examples, the fault detection blockperforms an overall SPRT algorithm that returns a Boolean output 0 (nochange) or 1 (change). When an overall change is identified, the changeanalysis block 320 generates an output indicating that a change hasoccurred, and also provides individual SPRT outputs W₁, W₂, W₃ that thefault identifier 340 uses to isolate or identify the specific fault atissue.

In a specific example using SPRT, the approach can be understood as therepeated testing of two alternative hypotheses about the testedparameter:

$\begin{matrix}\begin{array}{l}{H_{0}:\mspace{6mu}\mspace{6mu}\theta = \theta_{0}\mspace{6mu}\mspace{6mu}\{ {\text{Null}\mspace{6mu}\text{Hypothesis}} \}} \\{H_{1}:\mspace{6mu}\mspace{6mu}\theta = \theta_{1}\mspace{6mu}\mspace{6mu}\{ {\text{Change}/\text{Fault}} \}}\end{array} & \text{­­­\{Eq. 27\}}\end{matrix}$

As long as the decision is in favor of H₀, the test continues. Testingmay or may not stop upon a decision in favor of H₁. For example, testingmay not stop to track a self-healing condition (a valve temporarilystuck). Where the outcome of the analysis is used to modify other systemparameters, or analyses, if the decision reverts to H₀ then the modifiedsystem parameters or analyses may be reverted to their prior state. Thedecision rule can be written in a recursive manner as shown by Equation28:

$\begin{matrix}\begin{array}{l}{d\mspace{6mu} = \mspace{6mu}\mspace{6mu}\text{0}\mspace{6mu}\text{if}\mspace{6mu}\text{S}_{\text{k+1}}\mspace{6mu} \geq \text{0}\mspace{6mu}\text{or}} \\{d\mspace{6mu} = \mspace{6mu}\mspace{6mu} 1\mspace{6mu}\text{if}\mspace{6mu}\text{S}_{\text{k+1}}\mspace{6mu} = \mspace{6mu}\text{ε}}\end{array} & \text{­­­\{Eq. 28\}}\end{matrix}$

Where the recursive term S_(k+1) is defined as shown in Equation 29:

S_(k+1) =  min (ε, S_(k)+ s_(k)) if S_(k) + s_(k) > 0 or

$\begin{matrix}{\text{S}_{\text{k+1}}\mspace{6mu} = \mspace{6mu}\mspace{6mu}\max( {0,\mspace{6mu}\text{S}_{\text{k}}\text{+}\mspace{6mu}\text{s}_{\text{k}}} )\mspace{6mu}\text{if}\mspace{6mu}\text{S}_{\text{k}}\mspace{6mu} + \mspace{6mu}\text{s}_{\text{k}}\mspace{6mu} \leq \mspace{6mu} 0} & \text{­­­\{Eq. 29\}}\end{matrix}$

Where, in these two expressions, S_(k) is the log-likelihood ratio, andε is the threshold alarm value. The log-likelihood ratio S_(k) isdefined as:

$\begin{matrix}{s_{k}\mspace{6mu} = \mspace{6mu} ln( \frac{p( {g| \theta_{1} )} )}{p( {g| \theta_{0} )} )} )} & \text{­­­\{Eq. 30\}}\end{matrix}$

Where p(•) is the probability density function (PDF). The PDF may beparameterized using a common model, for example, a real valued randomvariable having a normal distribution. In some examples, the PDF can bethus parameterized using a mean (µ) and standard deviation (σ). Then theparameter vector can be represented as θ₀ = [µ, σ] for the nullhypothesis H₀. The alternative hypothesis H₁ is parameterized as θ₁ =[µ_(a), σ_(a)]. The H₀ PDF, when normalized, has parameters µ̅ = 0, σ̅ =1, and is given in Equation 31:

$\begin{matrix}{p( {g| {\mu,\sigma} )} )\mspace{6mu} = \mspace{6mu}\frac{1}{\sqrt{2\pi}}e^{- \frac{1}{2}x^{2}}} & \text{­­­\{Eq. 31\}}\end{matrix}$

Where x is the vector of realizations, and the expression shown is astandard normal distribution. Other distributions may be used; asimplified algorithm is provided here for illustrative purposes. The H₁PDF, when normalized, has parameters µ_(a), σ_(a), and is given as:

$\begin{matrix}{p( {g| {\mu_{a},\sigma} )_{a}} )\mspace{6mu} = \mspace{6mu}\frac{1}{\sigma_{a}\sqrt{2\pi}}e^{- \frac{1}{2}{(\frac{x - \mu_{a}}{\sigma_{a}})}^{2}}} & \text{­­­\{Eq. 32\}}\end{matrix}$

An illustrative SPRT method is shown as a process flow in FIGS. 6A-6B.

Starting with FIG. 6A, the system relies on a probability model 400useful for determining whether two signals are equal, where the model isbased on input engine data and calibration parameters. As noted above,the probability model in this example may be parameterized using a mean(µ) and standard deviation (σ). Signal 1 and Signal 2 are obtained andcompared at 402 (by subtraction in this example), with the differencethen being compared to the mean difference at 404 (again by subtractionin this example). The output form 404 is divided by the standarddeviation at 406, and the result is provided to the SPRT analysis 410.Next, the two PDFs for the null hypothesis 420 and the alternativehypothesis 422 are compared to the output of block 406. The statisticaldata is output to 424, and the log is then taken at 426, implementingEquation 30 to provide a result, A.

Turning to FIGS. 6B, A is then compared to S_(k). Upper and lower limitsare applied to the difference between A and S_(k). as indicated at 440,yielding the output S_(k+1). The value of S_(k+1) is compared to thethreshold 414 for identifying a signal inequality. Threshold 414 willhave been generated in a calibration procedure, an example of which isshown below. If the threshold 414 is exceeded at block 442, a signaldetecting inequality is generated, as indicated at 444.

The value for S_(k+1) is also provided to a step delay block at 450 foruse in a subsequent iteration. Block 450 generates the new value forS_(k+1) using Equation 29. Optionally, block 450 may reset upon faultdetection, as indicated in the Figure by the broken line. As noted at432, the null hypothesis is rejected if S_(k+1) exceeds the threshold inblock 442.

Turning back to FIG. 5 , within block 320 each of the features isdirected to a separately configured Change Probability (CP) Model. Insome examples, each such block 322 has a separately defined andcalibrated SPRT analysis. Thus, there is a 1:1 ratio of calibrated SPRTanalyses to feature indicators 304, as each feature indicator 304 isseparately analyzed. For calibration of each block 322, the separatefeature indicators can be analyzed individually.

For example, calibration can be performed for each SPRT method bystarting with the generation of the nominal error signal for eachnon-fault case (null hypothesis). Equation 33 may be used, for example:

$\begin{matrix}{x_{0}\mspace{6mu} = \mspace{6mu} Signal_{1{|{Fault\_ Off})}}\mspace{6mu} - \mspace{6mu} Signal_{2{|{Fault\_ Off})}}} & \text{­­­\{Eq. 33\}}\end{matrix}$

Using Feature Set 1, for example, calibrating an SPRT for I₁ may beperformed using m_(A,EGRv) as Signal₁, and m_(A,λ) as Signal₂, each asdetermined in a non-fault condition while the entering conditions aremet. The mean (µ) and standard deviation (σ) can each be calculated forx₀, allowing normalization of x₀.

Next, the error signals for H₁ is calculated for each fault underanalysis. For example, assuming three faults A, B, C, separate errorsignals for each can be calculated as shown by Equations 34-36:

$\begin{matrix}{x_{1,{\{ A\}}} = Signal_{{(1|}{\{ A\}}Fault\_ On} - Signal_{{(2|}{\{ A\}}Fault\_ On}} & \text{­­­\{Eq. 34\}}\end{matrix}$

$\begin{matrix}{x_{1,{\{ B\}}} = Signal_{{(1|}{\{ B\}}Fault\_ On} - Signal_{{(2|}{\{ B\}}Fault\_ On}} & \text{­­­\{Eq. 35\}}\end{matrix}$

$\begin{matrix}{x_{1,{\{ C\}}} = Signal_{{(1|}{\{ C\}}Fault\_ On} - Signal_{{(2|}{\{ C\}}Fault\_ On}} & \text{­­­\{Eq.36\}}\end{matrix}$

In an example, the faults may be A: Aftertreatment Fault, B: EGR Fault,and C: Balance valve fault, using the airpath example provided above.Normalized values for each of the separate error signals are createdusing the mean and standard deviation of x₀, for example:

$\begin{matrix}{{\overline{x}}_{1,{\{ A\}}} = \frac{x_{1,{\{ A\}}} - \mu}{\sigma}} & \text{­­­\{Eq. 37\}}\end{matrix}$

Similar normalization would be calculated for the remaining errorsignals. For each of the faulty realizations, mean and standarddeviation can then be computed providing two data sets of Δ = {µ_(a{A}),µ_(a{B}), µ_(a{C})} and Σ={σ_(a{A}), σ_(a{B}), σ_(a{c})}. Next, the PDFfor each of the nominal (null hypothesis) and faulty realizations arethen computed, and S_(k+1) for each PDF is calculated. A threshold alarmvalue for each SPRT is then selected, with reference to the Expert Table(FIG. 7 , below) to ensure the desired logical states can be achieved.The global tuning for each of the alternative hypothesis is thenselected from the sets of Δ and Σ for the faulty realizations. Theresult, in this example, is that each alternative hypothesis for eachSPRT has an alarm value with a cumulated sum S_(k+1). Finally, theExpert Table logical states are confirmed by evaluation of thenon-selected tunings.

FIG. 7 illustrates an Expert Table for an airflow system associated withan engine. In the example shown, the Expert Table 500 is filled with anumber of modes at 502, with one No Fault mode and several Fault modes.A set of SPRTs is illustrated in the remaining columns at 510. Each SPRTprovides a Boolean 0 or 1 output in this particular example. In thenominal or no-fault state, each SPRT returns a 0. The Fault modes areidentified by assessment of the combination of SPRTs that return a 1, asillustrated. For example, Mode 3 is identified in response to bothSPRT(I) 512 and SPRT(II) 514 being present, while Mode 4 requiresSPRT(II) 514 but not SPRT(I) 512, all without SPRT(III) 516 beingpresent. Thus each combination is particular to a select mode.

The table in FIG. 7 may be augmented with additional lines to accountfor any and all combinations of SPRT events. More than one line in thetable may be linked to a particular Mode, if desired, and/or more thanone Mode may be linked to a particular combination. In some examples,both Mode 2 and Mode 3 may be triggered if all three SPRTs are present,assuming that mitigations are available for both faults associated withthose modes and assuming all mitigations can be performedsimultaneously. On the other hand, in other examples, a system may beunable to assure continued operation within operating boundaries(whether for safety, performance, environmental, emissions or otherreasons) when multiple Faults are present, causing the system to issuean overall error/fault warning or requiring shut-down in response tosome combinations of SPRTs. Such determinations may vary byimplementation.

The example shown in FIG. 7 is for a system such as shown in FIG. 2 ,having a twin scroll turbine and balance valve. For a system as in FIG.1 (lacking the twin scroll turbine and balance valve), the Expert Tableof FIG. 7 can be simplified by removing the fourth Fault mode. In someexamples, the EGR Fault (Mode 3) may be identified using a logical “OR”for SPRT(I) and SPRT(II); in other examples, a Boolean 1 for both ofSPRT(I) or SPRT(II) may be required for identifying EGR fault. Asindicated in the description of the modes at 502, the Faults may belinked to A, B, and C, as used above.

FIG. 8 illustrates another Expert Table, this time directed at bothfault identification and health degradation. Referring first to FIG. 5 ,there may be any number of CP Models and feature inputs. Some featureinputs may be as illustrated above with respect to FIG. 7 , and directedto the identification of actual faults. One feature input may bemonitored by more than one CP Model, if desired. In FIG. 8 , I₁ ofFeature Set 1, above, is analyzed for change using a plurality ofdifferent thresholds, with one such threshold selected for largedivergence between the two mass air flows and indicative of a fault. Inthis example, the Expert Table 550 of FIG. 8 uses SPRT(I), at 552, tomonitor for a large change indicating EGR Fault, as indicated by Mode 2.Another CP Model, shown in FIG. 8 as SPRT(II) may be calibrated andtuned to identify smaller gradations of change. In the Expert Table 550,when SPRT(I) 552 returns a Boolean 1, EGR Fault is identified (whetherSPRT(II) returns a 1 or a 0 may be considered as well, if desired). If,instead, only SPRT(II) is triggered, an EGR Aging flag is identified.The Fault/Health Mitigator 350 (FIG. 5 ) may then respond by calling forreconfiguration of one or more parameters used in the State Observer 300and/or the Optimizer (230 in FIG. 4 ) to adjust how operations areperformed to account for the component aging. With models updated,SPRT(II) 554 (FIG. 8 ) is reset, and again monitors for furtherincremental change. In some examples, the State Observer may runseparate calculations for certain parameters to maintain both anoriginal configuration analysis, which would enable SPRT(I) in FIG. 8 tomonitor for changes relative to the original configuration to identifyFaults, while SPRT(II) monitors for changes relative to the currentconfiguration to identify aging.

FIG. 9 shows how health degradation may be tracked using probabilitydominance. Here, the plurality of W factors that are provided to theFault Identifier (340 in FIG. 5 ) are parsed, with the correlationbetween each W factor and the condition it identifies noted. Thus, foreach of a set of components A, B, C, D 600, the change model outputs{W₁, W₂ ... W_(n)} 602 are rated as indicating a likelihood of faultand/or present state information. In some examples, the change modeloutputs are continuous variables. Each value for W may be normalized,for example, in a range of 0 to 1, and these may be bracketed as shownat 604 into Beginning of Life (BOL), Full Useful Life, and End of Life(EOL), as desired. Linear normalization need not be used. When, forexample, W₁ is in the BOL range, the components whose health isindicated by W₁ have a value assigned and entered in the relevantcells - here, row 610 for component A, and row 614 for component C. Inthis example, an approach is taken where the lowest value assigned toany component is used as that component’s health status. Therefore,component A is deemed to be in its BOL range due to the value for W₁,even though other applicable change model outputs (W₃ and W_(n)) havehigher values; other approaches may be used including blended, averaged,etc. With the example shown, component B would be deemed to be in itsfull useful life, no longer at BOL but not yet at EOL (assuming no otherW factors are applicable), while component D is found to be at EOL, asthe lowest W value of 0.75 is indicative of EOL. Over time, it may beexpected that one or more of the W values will increase due to aging ofthe system components A-D, as the change model outputs continue totrack. When a component reaches the EOL boundary, a system alert can beissued to the user of the system or vehicle, and or may be communicatedto a central database for a fleet of vehicles, for example. In addition,as a component reaches any relevant boundary, mitigations discussedabove for managing system operation when a component has reached EOL canbe implemented.

It may also be noted that the table at 604 may include both aging/healthfactors as well as fault-related health factors. Thus, for example, oneor more of the W factors may relate to aging and health, while othersrelate to faults. A specific example may be, for example, a factor thatconsiders the capability of a valve to reach its full range of motionwhen actuated, something that can be monitored as well using an SPRT by,for example, observing the extremity of motion of a given valve. Inaddition, flow through the valve, referenced to a control signalapplicable to the valve, may be compared to modeled operation for thevalve and, when the flow does not match the model as determined using anSPRT or other change analysis, valve failure or blockage may beidentified. Thus a single component may be monitored for each of agingand fault using separate metrics reflected in the expert table..

In another example, each change model output is a Boolean value, and,reliant on the use of separate change models corresponding to Faults orAging increments, different determinations as to the meaning of anidentified change can be flagged as shown. That is, for component A, thepresence of a Boolean 1 for W₁ indicates the component is stilloperating at its beginning of life; on the other hand, a Boolean 1 forW_(n) indicates component A is at the end of life or is presenting afault. The Boolean approach may also be understood by reference to FIGS.7-8 .

Each of these non-limiting examples can stand on its own, or can becombined in various permutations or combinations with one or more of theother examples.

The above detailed description includes references to the accompanyingdrawings, which form a part of the detailed description. The drawingsshow, by way of illustration, specific embodiments. These embodimentsare also referred to herein as “examples.” Such examples can includeelements in addition to those shown or described. However, the presentinventors also contemplate examples in which only those elements shownor described are provided. Moreover, the present inventors alsocontemplate examples using any combination or permutation of thoseelements shown or described (or one or more aspects thereof), eitherwith respect to a particular example (or one or more aspects thereof),or with respect to other examples (or one or more aspects thereof) shownor described herein.

In the event of inconsistent usages between this document and anydocuments so incorporated by reference, the usage in this documentcontrols.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” Moreover, in theclaims, the terms “first,” “second,” and “third,” etc. are used merelyas labels, and are not intended to impose numerical requirements ontheir objects.

Method examples described herein can be machine or computer-implementedat least in part. Some examples can include a computer-readable mediumor machine-readable medium encoded with instructions operable toconfigure an electronic device to perform methods as described in theabove examples. An implementation of such methods can include code, suchas microcode, assembly language code, a higher-level language code, orthe like. Such code can include computer readable instructions forperforming various methods. The code may form portions of computerprogram products. Further, in an example, the code can be tangiblystored on one or more volatile, non-transitory, or non-volatile tangiblecomputer-readable media, such as during execution or at other times.Examples of these tangible computer-readable media can include, but arenot limited to, hard disks, removable magnetic or optical disks,magnetic cassettes, memory cards or sticks, random access memories(RAMs), read only memories (ROMs), and the like.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) may be used in combination with each other. Otherembodiments can be used, such as by one of ordinary skill in the artupon reviewing the above description. The Abstract is provided to complywith 37 C.F.R. § 1.72(b), to allow the reader to quickly ascertain thenature of the technical disclosure. It is submitted with theunderstanding that it will not be used to interpret or limit the scopeor meaning of the claims.

Also, in the above Detailed Description, various features may be groupedtogether to streamline the disclosure. This should not be interpreted asintending that an unclaimed disclosed feature is essential to any claim.Rather, innovative subject matter may lie in less than all features of aparticular disclosed embodiment. Thus, the following claims are herebyincorporated into the Detailed Description as examples or embodiments,with each claim standing on its own as a separate embodiment, and it iscontemplated that such embodiments can be combined with each other invarious combinations or permutations. The scope of the protection shouldbe determined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

What is claimed is:
 1. A configurable controller for controlling aphysical plant having each of: an engine with an intake manifold,combustion chamber receiving air from the intake manifold, and anexhaust manifold having first and second parts and allowing exhaust toexit the combustion chamber; an airflow system comprising a turbochargerhaving a compressor for providing compressed air to the intake manifoldand a twin scroll turbine with a first scroll coupled by a first exhaustpath to the first part of the exhaust manifold and a second scrollcoupled by a second exhaust path to the second part of the exhaustmanifold, with a balance valve controllably allowing airflow between thefirst and second exhaust paths; a plurality of actuators for controllingoperation of the engine and airflow system, and a plurality of sensorsfor observing a plurality of characteristics of the engine and airflowsystem; the configurable controller comprising: a state observerconfigured to capture a current state of the physical plant bycommunication with the plurality of sensors; a feature calculatorconfigured to receive current state data from the state observer andcalculate at least one feature parameter reflecting a state of health ofat least one of component of the physical plant, the at least onefeature parameter including a first feature parameter related to massair flow, and a second feature parameter related to exhaust manifold airpressure; and a fault isolator configured to identify a component of thephysical plant that is subject to a fault or health degradation, and togenerate a fault indicator indicating the identified fault, byidentifying a balance valve fault if the second feature parameter haschanged, but the first feature parameter has not changed.
 2. Theconfigurable controller of claim 1 further comprising: an optimizerconfigured to optimize behavior of the physical plant using at least theactuators; and a mitigator configured to modify the optimizer responsiveto the fault indicator, wherein: the optimizer is configured to use aplurality of factors to optimize behavior of the physical plant, eachfactor associated with a weight, and, prior to the fault indicatorindicating an identified fault, the optimizer uses a first set ofweights; and the mitigator is configured to respond to the faultindicator indicating an identified fault by modifying or replacing thefirst set of weights to thereby modify the optimizer.
 3. Theconfigurable controller of claim 1 further comprising an optimizerconfigured to optimize behavior of the physical plant using at least theactuators by: relying on a first model when the fault isolator fails togenerate the fault indicator; and relying on a second model when thefault isolator generates the fault indicator.
 4. The configurablecontroller of claim 1 further comprising a mitigator configured tomodify the state observer responsive to the fault indicator, wherein:the state observer is configured to apply one or more tuning parametersto monitor the current state of the physical plant; and the mitigator isconfigured to respond to the fault indicator by modifying or replacingone or more of the tuning parameters used by the state observer.
 5. Theconfigurable controller of claim 1 wherein the at least one featureparameter further comprises a third feature parameter related topressure downstream of the turbine, and the airflow system comprises anexhaust gas recirculation (EGR) valve allowing flow of exhaust from theexhaust manifold to return to the intake manifold, and the faultisolator is configured to: determine the fault is with the EGR valve inresponse to finding that both of the first and second feature parametershave changed; or determine the fault is with the aftertreatment if thethird feature parameter has changed.
 6. The configurable controller ofclaim 5 further comprising a health estimator configured to receive thefirst, second and third feature parameters and, subject to one or moreentering conditions, apply a change probability model to the first,second, and third feature parameters to determine whether any of thefirst, second or third feature parameters has changed to an extentindicative of a fault and, if so, to generate a change indicator; andthe fault isolator is responsive to the change indicator to identifywhich component of the physical plant is subject to a fault or healthdegradation.
 7. The configurable controller of claim 1 furthercomprising a health estimator configured to receive the first and secondfeature parameters and, subject to one or more entering conditions,apply a change probability model to the first and second featureparameters to determine whether any of the first or second featureparameters has changed to an extent indicative of a fault and, if so, togenerate a change indicator; and the fault isolator is responsive to thechange indicator to identify which component of the physical plant issubject to a fault or health degradation.
 8. The configurable controllerof claim 1 wherein the airflow system comprises an exhaust gasrecirculation (EGR) valve allowing flow of exhaust from the exhaustmanifold to return to the intake manifold, and the fault isolator isconfigured to determine the fault is with the EGR valve in response tofinding that both of the first and second feature parameters havechanged.
 9. A system comprising the configurable controller of claim 1and the physical plant having each of: an engine with an intakemanifold, combustion chamber receiving air from the intake manifold, andan exhaust manifold having first and second parts and allowing exhaustto exit the combustion chamber; an airflow system comprising aturbocharger having a compressor for providing compressed air to theintake manifold and a twin scroll turbine with a first scroll coupled bya first exhaust path to the first part of the exhaust manifold and asecond scroll coupled by a second exhaust path to the second part of theexhaust manifold, with a balance valve controllably allowing airflowbetween the first and second exhaust paths; a plurality of actuators forcontrolling operation of the engine and airflow system, and a pluralityof sensors for observing a plurality of characteristics of the engineand airflow system, wherein the configurable controller is coupled to atleast the plurality of actuators and the plurality of sensors.
 10. Anengine system comprising: an engine having an intake manifold and anexhaust manifold, with a combustion chamber therebetween into which afuel quantity is provided; an airflow system coupled to the engine, theairflow system having a turbocharger including a compressor and aturbine, and an exhaust gas recirculation (EGR) valve, the turbine beinga twin scroll turbine having first and second scrolls coupled to firstand second exhaust flow paths and a balance valve between the first andsecond exhaust flow paths; an aftertreatment system including a Lambdasensor; and a configurable controller for controlling the engine, theairflow system, and the aftertreatment system, comprising: a stateobserver configured to capture the current state of the engine, airflowsystem, and aftertreatment system; a feature calculator configured tocalculate a first feature parameter related to mass air flow, and asecond feature parameter related to exhaust manifold air pressure usingdata received from the state observer; and a fault isolator configuredto identify a system component that is subject to a fault using at leastthe first feature parameter and the second feature parameter byidentifying a balance valve fault in response to a change in the secondfeature parameter without a change in the first feature parameter, andgenerate a fault indicator indicating the identified fault.
 11. Thesystem of claim 10, wherein the configurable controller further includesa health estimator configured to receive the first and second featureparameters and, subject to one or more entering conditions, apply achange probability model to the first and second feature parameters todetermine whether any of the first or second feature parameters haschanged to an extent indicative of a fault and, if so, to generate achange indicator; and wherein the fault isolator is responsive to thehealth estimator generating a change indicator to identify the systemcomponent that is subject to a fault.
 12. The system of claim 10,wherein the fault isolator is configured to identify an EGR valve faultin response to both the first feature parameter and the second featureparameter changing.
 13. The system of claim 10, wherein the featurecalculator is configured to calculate a third feature parameter relatedto related to pressure downstream of the turbine, wherein the faultisolator is configured to identify: an EGR valve fault in response toboth the first feature parameter and the second feature parameterchanging; and an aftertreatment fault in response to the third featureparameter changing.
 14. The system of claim 10, wherein the configurablecontroller further includes an optimizer configured to optimize behaviorof the engine, airflow system, and aftertreatment system using thecurrent state of the engine and a selected one of at least first andsecond models, wherein the optimizer is configured to change which ofthe at least first and second models is selected in response to thefault isolator identifying a component subject to fault.
 15. A method ofoperation in an engine system comprising: an engine having an intakemanifold and an exhaust manifold, with a combustion chamber therebetweeninto which a fuel quantity is provided; an airflow system coupled to theengine, the airflow system having a turbocharger including a compressorand a turbine, and an exhaust gas recirculation (EGR) valve, the turbinebeing a twin scroll turbine having first and second scrolls coupled tofirst and second exhaust flow paths and a balance valve between thefirst and second exhaust flow paths; an aftertreatment system includinga Lambda sensor; and a configurable controller for controlling theengine, the airflow system, and the aftertreatment system, theconfigurable controller including each of a state observer, a featurecalculator, and a fault isolator, the method comprising: the stateobserver capturing a current state of the engine, airflow system, andaftertreatment system; the feature calculator calculating at least afirst feature parameter related to mass air flow, and a second featureparameter related to exhaust manifold air pressure using data receivedfrom the state observer; and the fault isolator identifying a balancevalve fault in response to a change in the second feature parameterwithout a change in the first feature parameter, and generating a faultindicator indicating the identified fault.
 16. The method of claim 15,wherein the configurable controller further includes a health estimator,and the method comprises: the health estimator receiving the first andsecond feature parameters and, subject to one or more enteringconditions; the health estimator applying a change probability model tothe first and second feature parameters to determine whether any of thefirst or second feature parameters has changed to an extent indicativeof a fault; and in response to the health estimator determining that atleast one of the first and second feature parameters has changed, thehealth estimator generating a change indicator and communicating thechange indicator to the fault isolator; and wherein the fault isolatoris responsive to the health estimator generating a change indicator toidentify the system component that is subject to a fault.
 17. The methodof claim 15, wherein the configurable controller further includes anoptimizer configured to optimize behavior of the engine, airflow system,and aftertreatment system using the current state of the engine and aselected one of at least first and second models, wherein the optimizeris configured to change which of the at least first and second models isselected in response to the fault isolator identifying a fault in thebalance valve.
 18. The method of claim 15, wherein the configurablecontroller further includes an optimizer configured to rely on aplurality of factors to optimize behavior of the physical plant bycontrol of the actuators, each factor associated with a weight, whereinthe method includes: before to the fault isolator identifies the balancevalve fault, the optimizer using a first set of weights; and after thefault isolator identifies the balance valve fault, the optimizer using asecond set of weights different from the first set of weights.
 19. Themethod of claim 15, wherein the configurable controller further includesa mitigator configured to modify the state observer responsive to thefault indicator, and the state observer is configured to apply one ormore tuning parameters to monitor the current state of the physicalplant; the method further comprising: before to the fault isolatoridentifies the balance valve fault, the state observer uses a first setof tuning parameters; and in response to the fault isolator identifyingthe balance valve fault, the mitigator changes at least one of thetuning parameters used by the state observer.